In recent years, decoding emotional states from brain activity has been widely studied. Most existing studies performed emotion decoding based on either fMRI or EEG signals. In fMRI-based emotion decoding, [1]} decoded four affective dimensions with logistic regression with percent signal change features; [2]} used linear regression to make predictions separately for each of the 34 emotion categories and each of the 14 affective dimensions based on the responses of ROI voxels; [3]} utilized support vector machine (SVM) to build the relationship between four emotion categories and brain activity in the auditory and motor cortex. Although they have made some progress in emotion decoding, they can only use simple linear models limited by high dimensional features. In EEG-based emotion decoding, [4]} used a dynamical Graph Convolutional Networks (GCNs) to model the multichannel EEG features and performed EEG emotion classification; [5]}, [6]} used contrastive learning and domain adaptation respectively for cross-subject EEG-based emotion recognition; [7]}, [8]} both took the discrepancy of bi-hemisphere into consideration for emotion decoding from EEG-based emotional datasets. Despite the complex models and scenarios, they still performed multi-class emotion decoding with coarse-grained emotion categories. Compared with EEG-based methods, fMRI-based studies can perform more fine-grained emotion decoding due to its higher spatial resolution but easily suffers from overfitting. Our method belongs to fMRI-based emotion decoding and we perform dimensionality reduction to the original fMRI data which can alleviate overfitting when complex models such as Deep Neural Networks (DNNs) are used. We also achieve multi-label emotion decoding which means decoding a set of emotional states simultaneously.
